Clustering Time Series Forecasting Model for Grouping Provinces in Indonesia Based on Granulated Sugar Prices

Fida Fariha Amatullah; Erdanisa Aghnia Ilmani; Anwar Fitrianto; Erfiani Erfiani; L. M. Risman Dwi Jumansyah

doi:10.30871/jaic.v9i1.8840

Fida Fariha Amatullah Prodi Statistika dan Sains Data, Sekolah SMI, IPB University
Erdanisa Aghnia Ilmani Prodi Statistika dan Sains Data, Sekolah SMI, IPB University
Anwar Fitrianto Prodi Statistika dan Sains Data, Sekolah SMI, IPB University
Erfiani Erfiani Prodi Statistika dan Sains Data, Sekolah SMI, IPB University
L. M. Risman Dwi Jumansyah Prodi Statistika dan Sains Data, Sekolah SMI, IPB University

DOI: https://doi.org/10.30871/jaic.v9i1.8840

Keywords: Clustering Time Series, Time Series Analysis, Granulated Sugar Prices, ARIMA Models, Forecasting Models

Abstract

Clustering time series is the process of organizing data into groups based on similarities in specific patterns. This research uses the prices of granulated sugar in each province of Indonesia. According to USDA reports, sugar consumption in Indonesia in 2023 reached 7.9 million tons. On April 26, 2024, the price of granulated sugar peaked in the Papua Mountains at Rp29,320 per kg, while the lowest price was recorded in the Riau Islands at Rp16,460 per kg. The research aims to cluster provinces based on the characteristics of granulated sugar prices and to use forecasting models for each group. Two groups were formed based on the price patterns of granulated sugar over time. The provinces of Papua and West Papua are in group 2, while the other 30 provinces are in group 1. The best model developed using the auto ARIMA method is ARIMA (2, 1, 0), with a MAPE value of 2.36% for cluster 1, and ARIMA (1, 1, 1), with a MAPE value of 2.59% for cluster 2. These values are less than 10%, indicating that the models built using the auto ARIMA method for clusters 1 and 2 are suitable for forecasting.

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